Information retrieval using hierarchical dirichlet processes

Authors:
Philip J. Cowans
Affiliations:
University of Cambridge, Cambridge, UK
Venue:
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2004

Citing 1
Cited 2

A language modeling approach to information retrieval

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval

Bayesian learning in text summarization

HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Hierarchical dirichlet trees for information retrieval

NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Quantified Score

Hi-index	0.00

Visualization

Abstract

An information retrieval method is proposed using a hierarchical Dirichlet process as a prior on the parameters of a set of multinomial distributions. The resulting method naturally includes a number of features found in other popular methods. Specifically, tf.idf-like term weighting and document length normalisation are recovered. The new method is compared with Okapi BM-25 [3] and the Twenty-One model [1] on TREC data and is shown to give better performance.